Classification method used in brain-computer interfaces

Abstract

The invention discloses a classification method used in brain-computer interfaces. The method comprises the steps of respectively calculating weight sum of each class of sample set characteristics according to the characteristics of a plurality of classes of sample sets in a training set, wherein the weight sum similarly obeys normal distribution, estimating a plurality of normal-distribution mean values and normal-distribution standard difference values, when classifying new samples, calculating weight sum of characteristics of the new samples, standardizing the calculated weight sum of the characteristics of the new samples according to the plurality of obtained normal-distribution mean values and normal-distribution standard difference values, obtaining a plurality of standardized values, sequencing absolute values of the plurality of standardized values, and enabling the sample set corresponding to the smallest value to serve as a class of the new samples. According to the classification method used in the brain-computer interfaces, the weight sum of the characteristics of the new samples is standardized through standard fraction, and then a classification boundary line is confirmed according to the standardized weight sum, and therefore the defect that according to a traditional linear discriminant analysis method, a classification identification rate is reduced under a condition that sample characteristics are different in distribution is overcome.

Description

technical field [0001] The invention belongs to the technical field of biomedical information, and in particular relates to a method for classifying and identifying EEG features in the field of brain-computer interface. Background technique [0002] Brain-Computer Interface (BCI) is developed in recent years and does not depend on the normal output pathways of the brain (ie, peripheral nerves and muscle tissue), it can realize direct communication between the human brain and the outside world (computer or other external devices) technology. Brain-computer interface technology can convert EEG signals into control signals to control external devices. Based on the brain-computer interface, a variety of enhanced control and communication systems can be developed to improve the quality of life of people with certain diseases, such as stroke , Parkinson's, motor neuron damage, etc. [0003] Usually, a brain-computer interface system based on EEG signals consists of four modules:...

AI Technical Summary

Problems solved by technology

Linear discriminant analysis assumes that the two types of data have the same covariance matrix, and the classification boundary line is obtained by finding the projection that maximizes the mean value of the two types of data while minimizing the variance within the class. The classification boundary line is only determined by the mean value of the two types of data after projection. The variance information of the two types of data after projection is ignored, resulting in inaccurate classification.

Images